Use of Oblique RGB Imagery and Apparent Surface Area of Plants for Early Estimation of Above-Ground Corn Biomass

Estimating above-ground biomass in the context of fertilization management requires the monitoring of crops at early stages. Conventional remote sensing techniques make use of vegetation indices such as the normalized difference vegetation index (NDVI), but they do not exploit the high spatial resolution (ground sampling distance < 5 mm) now achievable with the introduction of unmanned aerial vehicles (UAVs) in agriculture. The aim of this study was to compare image mosaics to single images for the estimation of corn biomass and the influence of viewing angles in this estimation. Nadir imagery was captured by a high spatial resolution camera mounted on a UAV to generate orthomosaics of corn plots at different growth stages (from V2 to V7). Nadir and oblique images (30° and 45° with respect to the vertical) were also acquired from a zip line platform and processed as single images. Image segmentation was performed using the difference color index Excess Green-Excess Red, allowing for the discrimination between vegetation and background pixels. The apparent surface area of plants was then extracted and compared to biomass measured in situ. An asymptotic total least squares regression was performed and showed a strong relationship between the apparent surface area of plants and both dry and fresh biomass. Mosaics tended to underestimate the apparent surface area in comparison to single images because of radiometric degradation. It is therefore conceivable to process only single images instead of investing time and effort in acquiring and processing data for orthomosaic generation. When comparing oblique photography, an angle of 30° yielded the best results in estimating corn biomass, with a low residual standard error of orthogonal distance (RSEOD = 0.031 for fresh biomass, RSEOD = 0.034 for dry biomass). Since oblique imagery provides more flexibility in data acquisition with fewer constraints on logistics, this approach might be an efficient way to monitor crop biomass at early stages.

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Classification of multi-temporal spectral indices for crop type mapping: a case study in Coalville, UK

The Journal of Agricultural Science ◽

10.1017/s0021859617000879 ◽

2018 ◽

Vol 156 (1) ◽

pp. 24-36 ◽

Cited By ~ 15

Author(s):

Y. Palchowdhuri ◽

R. Valcarce-Diñeiro ◽

P. King ◽

M. Sanabria-Soto

Keyword(s):

Spatial Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Reference Data ◽

High Spatial Resolution ◽

Classification Algorithms ◽

Type Mapping ◽

Crop Type ◽

Multi Temporal ◽

Very High

AbstractRemote sensing (RS) offers an efficient and reliable means to map features on Earth. Crop type mapping using RS at various temporal and spatial resolutions plays an important role spanning from environmental to economical. The main objective of the current study was to evaluate the significance of optical data in a multi-temporal crop type classification-based on very high spatial resolution and high spatial resolution imagery. With this aim, three images from WorldView-3 and Sentinel-2 were acquired over Coalville (UK) between April and July 2016. Three vegetation indices (VIs); the normalized difference vegetation index, the green normalized difference vegetation index and soil adjusted vegetation index were generated using red, green and near-infrared spectral bands; then a supervised classification was performed using ground reference data collected from field surveys, Random forest (RF) and decision tree (DT) classification algorithms. Accuracy assessment was undertaken by comparing the classified output with the reference data. An overall accuracy of 91% and κ coefficient of 0·90 were estimated using the combination of RF and DT classification algorithms. Therefore, it can be concluded that integrating very high- and high-resolution imagery with different VIs can be implemented effectively to produce large-scale crop maps even with a limited temporal-dataset.

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Fusion of MODIS and Landsat-Like Images for Daily High Spatial Resolution NDVI

Remote Sensing ◽

10.3390/rs12081297 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1297

Author(s):

Roberto Filgueiras ◽

Everardo Chartuni Mantovani ◽

Elpídio Inácio Fernandes-Filho ◽

Fernando França da Cunha ◽

Daniel Althoff ◽

...

Keyword(s):

Spatial Resolution ◽

Regression Models ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

High Spatial Resolution ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Bias Error ◽

Landsat 8 ◽

Modis Ndvi

One of the obstacles in monitoring agricultural crops is the difficulty in understanding and mapping rapid changes of these crops. With the purpose of addressing this issue, this study aimed to model and fuse the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) using Landsat-like images to achieve daily high spatial resolution NDVI. The study was performed for the period of 2017 on a commercial farm of irrigated maize-soybean rotation in the western region of the state of Bahia, Brazil. To achieve the objective, the following procedures were performed: (i) Landsat-like images were upscaled to match the Landsat-8 spatial resolution (30 m); (ii) the reflectance of Landsat-like images was intercalibrated using the Landsat-8 as a reference; (iii) Landsat-like reflectance images were upscaled to match the MODIS sensor spatial resolution (250 m); (iv) regression models were trained daily to model MODIS NDVI using the upscaled Landsat-like reflectance images (250 m) of the closest day as the input; and (v) the intercalibrated version of the Landsat-like images (30 m) used in the previous step was used as the input for the trained model, resulting in a downscaled MODIS NDVI (30 m). To determine the best fitting model, we used the following statistical metrics: coefficient of determination (r2), root mean square error (RMSE), Nash–Sutcliffe efficiency index (NSE), mean bias error (MBE), and mean absolute error (MAE). Among the assessed regression models, the Cubist algorithm was sensitive to changes in agriculture and performed best in modeling of the Landsat-like MODIS NDVI. The results obtained in the present research are promising and can enable the monitoring of dynamic phenomena with images available free of charge, changing the way in which decisions are made using satellite images.

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Integrating Optical and Microwave Satellite Observations for High Resolution Soil Moisture Estimate and Applications in CONUS Drought Analyses

Remote Sensing ◽

10.18282/rs.v7i1.500 ◽

2019 ◽

Vol 7 (1) ◽

Author(s):

Donglian Sun ◽

Yu Li ◽

Xiwu Zhan ◽

Chaowei Yang ◽

Ruixin Yang

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

High Spatial Resolution ◽

Weather Conditions ◽

Daily Basis ◽

Satellite Observations ◽

Drought Analysis

<strong>In this study, optical and microwave satellite observations are integrated to estimate soil moisture at high spatial resolution and applied for drought analysis in the continental United States. To estimate soil moisture, a new refined model is proposed to estimate soil moisture (SM) with auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed SM model using accumulated precipitation demonstrated close agreements with the </strong><strong>U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently, “flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively high spatial resolution, thus drought maps based on soil moisture anomalies can be obtained at high spatial resolution on daily basis and made the flash drought analysis and monitoring become possible.</strong>

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Complementarity between Textural and Radiometric Indices From Airborne and Spaceborne Multi VHSR Data: Disentangling the Complexity of Heterogeneous Landscape Matrix

Remote Sensing ◽

10.3390/rs11060693 ◽

2019 ◽

Vol 11 (6) ◽

pp. 693

Author(s):

Marc Lang ◽

Samuel Alleaume ◽

Sandra Luque ◽

Nicolas Baghdadi ◽

Jean-Baptiste Féret

Keyword(s):

Spatial Resolution ◽

Landscape Structure ◽

Vegetation Structure ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Habitat Degradation ◽

Strong Relationship ◽

Bare Soil ◽

Landscape Matrix

The quantitative characterization of landscape structure is critical to assess conservation, and monitor and manage biodiversity. The Mediterranean Basin is a biodiversity hotspot that illustrates the strong relationship between biodiversity and the complexity of the landscape mosaic. Our objective was to test the relevance of two textural indices and one radiometric index (the normalized difference vegetation index (NDVI)) to characterize vegetation structure. These indices could be used as indicators of vegetation composition and organization of four vertical strata when derived from airborne and Pléiades space-borne VHSR imagery. More specifically, we analyzed the influence of the spatial resolution and the radiometric information on the characterization of the landscape structure. Our results indicated that NDVI information at 0.5 m spatial resolution was necessary to be able to incorporate the heterogeneity of vegetation structure. Indices derived from lower resolution NDVI images or different radiometric information than airborne images also proved to be sensitive to vegetation fragmentation and composition. NDVI images brought out details on ligneous/herbs patterns while panchromatic image brought out more details on herbs/bare soil patterns. Combined textural and NDVI indices show strong potential for vegetation structure understanding, allowing detailed mapping. NDVI information shows good potential for applications related to landscape closure dynamics; related habitat degradation indicators caused by shrub encroachment. Panchromatic derived information, on the other hand, provides information relevant in applications focusing grazing management.

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Application of the vegetation index to urban planning

E3S Web of Conferences ◽

10.1051/e3sconf/202129102021 ◽

2021 ◽

Vol 291 ◽

pp. 02021

Author(s):

Nikolay Bondarenko ◽

Tatyana Lyubimova ◽

Yulya Reshetnikova

Keyword(s):

Urban Environment ◽

Spatial Resolution ◽

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

High Spatial Resolution ◽

Terrestrial Vegetation ◽

Surrounding Areas ◽

Source Of Information ◽

Degree Of Degradation

This article contains data on the change in the NDVI (Normalized Difference Vegetation Index) over a 10 year period from 2009 to 2019 obtained for identification of terrestrial vegetation using satellite images of high spatial resolution in the territory of the Novorossiysk agglomeration. The degree of degradation of the vegetation within the urban agglomeration and surrounding areas has shown the complexity of the overall structure due to finer gradations. Areas with oppressed vegetation connected both with the state of vegetation in this season of the year, and with the increase in the proportion of artificial objects were identified. Some special features of revegetation with due consideration of climate pattern are provided. The vegetation index data can serve as a source of information on the intensity of tree and shrub vegetation for making correct decisions regarding the development of modern urban environment.

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IMAGENS DE VEÍCULO AÉREO NÃO TRIPULADO APLICADAS NA OBTENÇÃO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA

ENERGIA NA AGRICULTURA ◽

10.17224/energagric.2021v36n1p111-122 ◽

2021 ◽

Vol 36 (1) ◽

pp. 111-122

Author(s):

Felipe de Souza Nogueira Tagliarini ◽

Mikael Timóteo Rodrigues ◽

Bruno Timóteo Rodrigues ◽

Yara Manfrin Garcia ◽

Sérgio Campos

Keyword(s):

Remote Sensing ◽

Unmanned Aerial Vehicle ◽

Spatial Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

High Spatial Resolution ◽

Spectral Response ◽

Infrared Sensors ◽

Aerial Vehicle ◽

E Mail

IMAGENS DE VEÍCULO AÉREO NÃO TRIPULADO APLICADAS NA OBTENÇÃO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA FELIPE DE SOUZA NOGUEIRA TAGLIARINI1, MIKAEL TIMÓTEO RODRIGUES2-3, BRUNO TIMÓTEO RODRIGUES1; YARA MANFRIN GARCIA1 E SÉRGIO CAMPOS1 1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas (FCA) - Universidade Estadual Paulista (UNESP), Avenida Universitária, nº 3780, Altos do Paraíso, CEP: 18610-034, Botucatu, São Paulo, Brasil. E-mail: [email protected]; [email protected]; [email protected]; [email protected] 2 Centro Universitário Dinâmica das Cataratas (UDC), Rua Castelo Branco, nº 440, Centro, CEP: 85852-010, Foz do Iguaçu, Paraná, Brasil. E-mail: [email protected] 3 Parque Tecnológico Itaipu (PTI), Avenida Tancredo Neves, nº 6731, Jardim Itaipu, Caixa Postal: 2039, CEP: 85867-900, Foz do Iguaçu, Paraná, Brasil. E-mail: [email protected]. RESUMO: O advento dos Veículos Aéreos Não Tripulados (VANT) como ferramenta no sensoriamento remoto possibilitou uma plataforma atuante em diferentes áreas para o mapeamento com elevada precisão e resolução. O objetivo deste estudo consistiu na análise do Índice de Vegetação por Diferença Normalizada (NDVI) para elaboração de mapa temático por meio de aerofotogrametria e fotointerpretação, com maior detalhamento da vegetação devido à altíssima resolução espacial alcançada com o uso de imagens coletadas por VANT em trecho do rio Lavapés, dentro dos limites da Fazenda Experimental Lageado no município de Botucatu-SP. As imagens foram obtidas por meio dos sensores MAPIR Survey3W RGB e Survey3W NIR/InfraRED, embarcados em VANT multirrotor 3DR SOLO. Para construção dos ortomosaicos RGB e NDVI, as imagens foram processadas no software Pix4Dmapper 3.0. O resultado do NDVI proporcionou transição bem nítidas entre os alvos bióticos (vegetação) e os alvos abióticos (corpo d'água, solo e edificações), e também entre a própria vegetação, possibilitando a distinção da vegetação de porte arbóreo, com maior vigor vegetativo, em relação a vegetação de porte herbáceo. As imagens com elevada resolução espacial coletadas por VANT, demonstraram flexibilidade de utilização, possuindo elevado potencial para o mapeamento de dinâmica da paisagem e a resposta espectral da vegetação. Palavras-chaves: drone, índice radiométrico, sensoriamento remoto IMAGES OF UNMANNED AERIAL VEHICLE APPLIED TO OBTAIN THE NORMALIZED DIFFERENCE VEGETATION INDEX ABSTRACT: The advent of Unmanned Aerial Vehicle (UAV) as a tool in remote sensing has enabled a platform acting in different areas for mapping with high precision and resolution. This study aimed to analyze the Normalized Difference Vegetation Index (NDVI) for the elaboration of thematic map through aerophotogrammetry and photointerpretation, with greater detail of vegetation due to high spatial resolution achieved with the use of images collected by UAV in a stretch of Lavapés river, inside the domains of Lageado Experimental Farm in the municipality of Botucatu-SP. The images were obtained through MAPIR Survey3W RGB and Survey3W NIR/InfraRED sensors, aboard a 3DR SOLO multirotor UAV. For constructing RGB and NDVI orthomosaics, the images were processed using Pix4Dmapper 3.0 software. The NDVI result provided a clear transition among biotic targets (vegetation) and abiotic targets (water, soil and buildings), and among the vegetation itself, with greater vegetative vigor, making possible the distinction of arboreal vegetation, in relation to herbaceous vegetation. The images with high spatial resolution collected by UAV demonstrated the flexibility of use, having high potential to mapping landscape dynamics and the spectral response of vegetation. Keywords: drone, radiometric index, remote sensing.

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Coastal Mangrove Response to Marine Erosion: Evaluating the Impacts of Spatial Distribution and Vegetation Growth in Bangkok Bay from 1987 to 2017

Remote Sensing ◽

10.3390/rs12020220 ◽

2020 ◽

Vol 12 (2) ◽

pp. 220 ◽

Cited By ~ 2

Author(s):

Han Xiao ◽

Fenzhen Su ◽

Dongjie Fu ◽

Qi Wang ◽

Chong Huang

Keyword(s):

Spatial Distribution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Mangrove Ecosystem ◽

Early Growth ◽

Growth Stages ◽

Human Disturbances ◽

Vegetation Growth ◽

Two Parameters ◽

The Impact

Long time-series monitoring of mangroves to marine erosion in the Bay of Bangkok, using Landsat data from 1987 to 2017, shows responses including landward retreat and seaward extension. Quantitative assessment of these responses with respect to spatial distribution and vegetation growth shows differing relationships depending on mangrove growth stage. Using transects perpendicular to the shoreline, we calculated the cross-shore mangrove extent (width) to represent spatial distribution, and the normalized difference vegetation index (NDVI) was used to represent vegetation growth. Correlations were then compared between mangrove seaside changes and the two parameters—mangrove width and NDVI—at yearly and 10-year scales. Both spatial distribution and vegetation growth display positive impacts on mangrove ecosystem stability: At early growth stages, mangrove stability is positively related to spatial distribution, whereas at mature growth the impact of vegetation growth is greater. Thus, we conclude that at early growth stages, planting width and area are more critical for stability, whereas for mature mangroves, management activities should focus on sustaining vegetation health and density. This study provides new rapid insights into monitoring and managing mangroves, based on analyses of parameters from historical satellite-derived information, which succinctly capture the net effect of complex environmental and human disturbances.

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Evaluation of a NIR camera for monitoring yield and nitrogen effect in sugarcane

Agronomía Colombiana ◽

10.15446/agron.colomb.v35n1.60852 ◽

2017 ◽

Vol 35 (1) ◽

pp. 82-91

Author(s):

Cesar Edwin García ◽

David Montero ◽

Hector Alberto Chica

Keyword(s):

Spatial Resolution ◽

Vegetation Index ◽

High Spatial Resolution ◽

Early Stage ◽

Vegetation Indices ◽

Visible Spectrum ◽

Crop Productivity ◽

Spectral Information ◽

Nitrogen Effect ◽

Sugarcane Yield

The main objective of the research carried out in the sugar productive sector in Colombia is to improve crop productivity of sugarcane. The rise of RPAS, together with the use of multispectral cameras, which allows for high spatial resolution images and spectral information outside the visible spectrum, has generated an alternative nondestructive technological approach to monitoring crop sugarcane that must be evaluated and adapted to the specific conditions of Colombia's sugar productive sector. In this context, this paper assesses the potential of a modified camera (NIR) to discriminate three varieties of sugarcane, as well as three doses of fertilization and estimating the sugarcane yield at an early stage, for the three varieties through multiple vegetation indices. In this study, no significant differences were found by vegetation index between fertilization doses, and only significant differences between varieties were found when the fertilization was normal or high. Likewise, multiple regressions between scores derived from vegetation indices after applying PCA and productivity produced determinations of up to 56%.

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An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

Earth Science Informatics ◽

10.1007/s12145-015-0217-3 ◽

2015 ◽

Vol 8 (2) ◽

pp. 327-335 ◽

Cited By ~ 37

Author(s):

Daniel Hölbling ◽

Barbara Friedl ◽

Clemens Eisank

Keyword(s):

Change Detection ◽

Spatial Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Detection Methods ◽

Detection And Attribution ◽

Object Based ◽

Wide Range ◽

Northern Taiwan

Abstract Earth observation (EO) data are very useful for the detection of landslides after triggering events, especially if they occur in remote and hardly accessible terrain. To fully exploit the potential of the wide range of existing remote sensing data, innovative and reliable landslide (change) detection methods are needed. Recently, object-based image analysis (OBIA) has been employed for EO-based landslide (change) mapping. The proposed object-based approach has been tested for a sub-area of the Baichi catchment in northern Taiwan. The focus is on the mapping of landslides and debris flows/sediment transport areas caused by the Typhoons Aere in 2004 and Matsa in 2005. For both events, pre- and post-disaster optical satellite images (SPOT-5 with 2.5 m spatial resolution) were analysed. A Digital Elevation Model (DEM) with 5 m spatial resolution and its derived products, i.e., slope and curvature, were additionally integrated in the analysis to support the semi-automated object-based landslide mapping. Changes were identified by comparing the normalised values of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI) of segmentation-derived image objects between pre- and post-event images and attributed to landslide classes.

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New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle

Remote Sensing ◽

10.3390/rs12244170 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4170

Author(s):

Pengfei Chen ◽

Fangyong Wang

Keyword(s):

Unmanned Aerial Vehicle ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Biomass Estimation ◽

Growth Stages ◽

Spectral Indices ◽

Green Band ◽

Validation Data ◽

Texture Parameters ◽

Aerial Vehicle

Although textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating cotton biomass and solving saturation problems at medium and high biomass levels. A nitrogen (N) fertilization experiment was established, and unmanned aerial vehicle optical images and field measured biomass data were obtained during critical cotton growth stages. Based on these data, two textural indices, namely the normalized difference texture index combining contrast and the inverse difference moment of the green band (NBTI (CON, IDM)g) and normalized difference texture index combining entropy and the inverse difference moment of the green band (NBTI (ENT, IDM)g), were proposed by analyzing the mechanism of texture parameters for biomass prediction and the law of texture parameters changing with biomass. These indices were compared with spectral indices commonly used for biomass estimation using independent validation data, such as the normalized difference vegetation index (NDVI). The results showed that the proposed textural indices performed better than the spectral indices with no saturation problems occurring. The combination of spectral and textural indices using a stepwise regression method performed better for biomass estimation than using only spectral or textural indices. This method has considerable potential for improving the accuracy of biomass estimations for the subsequent delineation of precise cotton management zones.

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